Over recent years, cell culture has become a core technology in the life sciences. The underlying science of cell culture is complicated, so that the effect of different treatments and growing conditions remains poorly understood. Many cell culture treatments are developed on a trial and error basis, perhaps by analogy with existing treatments. However, this approach is time-consuming, unreliable, and clearly inefficient.
Cell culture protocols which involve multiple discrete stages are particularly difficult to devise and optimise. Changing the treatment in one stage may affect the performance of a subsequent stage, so that devising optimal combinations of treatments is particularly challenging and requires large numbers of experiments. Such experiments can be performed by conventional cell culture, although methods with higher throughput such as automated cell culture are also known. These experiments may involve methods of miniaturising cell culture, such as the use of microfluidic platforms (e.g. “Differentiation-on-a-chip: A microfluidic platform for long-term cell culture studies”, Anna Tourovskaia, Xavier Figueroa-Masot and Albert Folch; Lab Chip, 2005, 5, 14-19), or cell arrays. There is a need in the art for methods to analyse data produced by high throughput cell culture techniques.
EP-A 1551954 and WO2007/023297, the contents of which are incorporated herein by reference, describe a technique in which cells are cultured in a large number of different units. Each unit may be formed from a bead with cells growing on the surface or in pores. The cell units (beads) are split into different groups and each group is subjected to a particular treatment. After this first stage (round), the cell units may be optionally pooled together again, and then split once again into new groups. The new groupings are then subjected to a second round of treatments. Further rounds of pooling, splitting, and treatment may follow. The cell units are optionally tagged during the culture treatments so that at the end of the experiment it is possible to deduce the sequence of treatments applied to a given cell unit. Cell units that have reached a desired endpoint, say the development of a particular cell type as judged by a screening assay, can be identified and the sequence of treatments to which they were exposed identified.
The number of cell units in these experiments may be very large—thousands or more. Likewise, the number of possible protocol combinations of treatments (protocols) to which different cell units have been exposed may also be large. For example, if there are 10 possible treatments at each of three stages, then this gives 1000 (103) potential protocols. If the experiment involves 50,000 cell units, then on average 50 beads will be exposed to each protocol.
Results from such large scale screening experiments require validation since these typically include false-positives, where a desired result is achieved but the outcome is spurious, and also false-negatives where a cell unit that follows a potentially productive protocol does not give a positive outcome. The number of false-negatives provides some measure of the efficiency of a given protocol, which might often be rather low (10% or less).
In general, the existing approach to analysing results from these large-scale cell culture experiments is to look for protocols that produced positive results in an endpoint screen. The successful protocols are then the subject of further experiments. This follow-up work may involve testing a larger number of cell units per protocol to give better statistics for the results, or a different experimental strategy, such as performing conventional or monolayer cell culture (rather than using small beads).
This follow-up work is relatively expensive and time-consuming to perform, especially if there are many protocols that appear to require further investigation. It would be helpful for the data analysis to be able to guide the selection of subsets of protocols for further investigation, and even to be able to predict the efficiency of these protocols a priori. For example, since experiments are often performed in parallel, e.g. multiple cell units are exposed to each protocol, a protocol may be particularly suitable for follow-up work if N or more replicates are positive in an endpoint screen (where N may be chosen as 1, 2, 3 . . . etc, depending on the particular circumstances).
One important goal of such experiments is to be able to control or direct the differentiation of cells towards a particular phenotype. For example, starting with stem cells, it may be desired to produce in culture a specific type of cell, for example red blood cells, heart muscle cells, or brain cells. The resulting specialised cells are then available for a wide variety of potential uses, including the modelling and investigation of biological systems, toxicity screening for drugs, screening for regenerative drug development and transplanting the cells into humans to replace dead or diseased cells, for example in the case of a stroke or spinal cord injury. Cell culture experiments can also be useful in a wide range of other applications.